# 1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track Source: [1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:55:03.570Z Authors: Wei Zhao, Binbin Chen, Weijie Chen, Shicai Yang, Di Xie, Shiliang Pu, Yueting Zhuang Published: 2023-01-12T03:50:46.000Z ## Abstract OOD-CV challenge is an out-of-distribution generalization task. To solve this problem in object detection track, we propose a simple yet effective Generalize-then-Adapt (G&A) framework, which is composed of a two-stage domain generalization part and a one-stage domain adaptation part. The domain generalization part is implemented by a Supervised Model Pretraining stage using source data for model warm-up and a Weakly Semi-Supervised Model Pretraining stage using both source data with box-level label and auxiliary data (ImageNet-1K) with image-level label for performance boosting. The domain adaptation part is implemented as a Source-Free Domain Adaptation paradigm, which only uses the pre-trained model and the unlabeled target data to further optimize in a self-supervised training manner. The proposed G&A framework help us achieve the first place on the object detection leaderboard of the OOD-CV challenge. Code will be released in https://github.com/hikvision-research/OOD-CV. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()